mlsanityData to 14 Oct 2026

ELODIN: Naming Concepts in Embedding Spaces

arXiv 2303.04001 ↗First posted 7 Mar 2023cs.CV cs.CL cs.GRcited by 0

Not on the map. Why?

Abstract (arXiv)

Despite recent advancements, the field of text-to-image synthesis still suffers from lack of fine-grained control. Using only text, it remains challenging to deal with issues such as concept coherence and concept contamination. We propose a method to enhance control by generating specific concepts that can be reused throughout multiple images, effectively expanding natural language with new words that can be combined much like a painter's palette. Unlike previous contributions, our method does not copy visuals from input data and can generate concepts through text alone. We perform a set of comparisons that finds our method to be a significant improvement over text-only prompts.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 28 of its 31 references that are in the map. First posted on arXiv 7 Mar 2023.

Builds on

28 of 31 references.

References · 28

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  3. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  4. 2020Denoising Diffusion Implicit Modelscited by 9,781
  5. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,585
  6. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,355
  7. 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,503
  8. 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,588
  9. 2022DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generationcited by 3,300
  10. 2015FaceNet: A Unified Embedding for Face Recognition and Clusteringcited by 2,849
  11. 2022InstructPix2Pix: Learning to Follow Image Editing Instructionscited by 2,611
  12. 2022An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversioncited by 2,245
16 more
  1. 2022Scaling Autoregressive Models for Content-Rich Text-to-Image Generationcited by 1,295
  2. 2022Imagic: Text-Based Real Image Editing with Diffusion Modelscited by 1,178
  3. 2022Multi-Concept Customization of Text-to-Image Diffusioncited by 1,106
  4. 2019Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space?cited by 675
  5. 2022Paint by Example: Exemplar-based Image Editing with Diffusion Modelscited by 489
  6. 2021GAN Inversion: A Surveycited by 367
  7. 2023Composer: Creative and Controllable Image Synthesis with Composable Conditionscited by 304
  8. 2023StyleGAN-T: Unlocking the Power of GANs for Fast Large-Scale Text-to-Image Synthesiscited by 212
  9. 2022Sketch-Guided Text-to-Image Diffusion Modelscited by 180
  10. 2022"This is my unicorn, Fluffy": Personalizing frozen vision-language representationscited by 95
  11. 2017Online and Linear-Time Attention by Enforcing Monotonic Alignmentscited by 85
  12. 2023Zero-shot Generation of Coherent Storybook from Plain Text Story using Diffusion Modelscited by 26
  13. 2022Comparing the latent space of generative modelscited by 12
  14. 2022Image Embedding for Denoising Generative Modelscited by 7
  15. 2022Extensible Prompts for Language Models on Zero-shot Language Style Customizationcited by 6
  16. 2023Controlled and Conditional Text to Image Generation with Diffusion Priorcited by 6

Built on by

Not cited yet.